Camtree Digital Library

Recent Submissions

  • ItemOpen Access
    AI-Driven Teaching Innovation: A Practical Exploration of the Programming Internship and Image Processing Undergraduate Programs
    (2026) Yang, Shuai
    Background: With the rapid advancement of AI, integrating AI tools into higher education has become a transformative trend. As a teacher teaching two undergraduate courses: Practice of Programming and Image Processing, I leveraged AI to enhance course design and pedagogy, addressing the growing demand for AI learning among students. Problems, Challenges, or Opportunities Addressed: The innovation tackled: (1) Outdated programming course lacking AI relevance; (2) Limited student engagement in traditional image processing courses; (3) The need to equip students with practical AI skills. AI integration presented opportunities to modernize content, improve interactivity, and foster interdisciplinary competencies. Methods for Development and Evaluation: For Practice of Programming, Python replaced C++ for AI alignment, and DeepSeek assisted in creating interactive coding examples (e.g., a mini-game); for Image Processing, AI tools (e.g., Photoshop, HuggingFace) and deep learning techniques (e.g., diffusion models) were embedded into lectures and assignments. Student feedback and project outcomes were analysed to evaluate effectiveness. Key Findings and Outcomes: Students demonstrated improved engagement and creativity, especially in AI-driven projects. AI tools streamlined instructor workflows (e.g., generating teaching materials via DeepSeek). The courses successfully bridged foundational knowledge and cutting-edge AI applications. Impact on Teaching Practice: The innovation reinforced the role of AI as both a pedagogical aid and a core skill for students. It highlighted the need for educators to continuously adapt courses and embrace AI tools to enhance learning outcomes and career readiness.
  • PublicationOpen Access
    AI-empowered intangible cultural heritage activation: innovative practice in higher education to build a "dual-core-driven" smart teaching system
    (2026) Wang, Hei; Liu, Yipin; Ying, Li
    Background: Higher education urgently needs to move from passive adoption of AI tools to proactive exploration. Focusing on the course Installation Art and Environmental Facilities A at Tianjin University, we integrate Intangible Cultural Heritage (ICH) with generative AI to cultivate students’ cultural confidence and computational creativity. Challenges & Opportunities: Traditional ICH education suffers from static content, low interactivity, and limited interdisciplinary integration. AI offers generative power, yet risks cultural dilution and ethical misuse. Method: We developed the “dual-core” system: (1) an 8848.86 automated design platform that embeds 14 textbooks and 81 papers into a dynamic knowledge graph; (2) a “five-step three-state” pedagogical model (resource preparation → content design → classroom implementation → assessment → auxiliary support). Tools included MidJourney, Stable Diffusion, and Doubao Vision Model. Data-driven assessment used radar charts (cultural, technical, social dimensions). Findings: Design cycle shortened from 4 weeks to 6 days (300 % efficiency gain); award rate rose from 20 % (2020) to 99 % (2024). Cultural recognizability increased 55%; 89% of under-prepared students improved significantly. The system has been adopted by 20 universities, benefiting 5000+ students. Impact on Teaching: Instructors shifted from knowledge transmitters to learning architects, orchestrating AI-human synergy and ethical reflection. A replicable paradigm for AI-enhanced design education has been established.
  • PublicationOpen Access
    Construction and Application of an AI Case-Based Virtual Simulation Intelligent Teaching Platform for Endodontics
    (2026) Yang, Yang; Yang, Deqin; Wei, Xiaoling; Hu, Yufeng; Huang, Yiwei; Hu, Linfeng; Zhao, Zhongqi
    Background: Endodontics emphasizes the organic integration of theoretical knowledge, clinical skills, and diagnostic reasoning. Traditional teaching models are insufficient in constructing highly immersive training environments and delivering personalized instruction. Virtual simulation platforms centered on artificial intelligence (AI) case libraries provide students with safe, controllable, and repeatable practical training environments through high-fidelity clinical scene restoration and interactive operational experiences, representing a crucial direction for overcoming teaching bottlenecks. Objective: This study aims to construct an intelligent teaching platform that deeply integrates artificial intelligence and virtual simulation technology, fully leveraging the core advantages of virtual simulation in clinical scene construction and immersive learning experiences, and systematically evaluate its effectiveness in addressing standardized clinical skills assessment, the creation of safe and repeatable training environments, and the design of personalized learning pathways. Methods: The research team developed an intelligent teaching system with virtual simulation technology as the underlying foundation and artificial intelligence as the intelligent driving core. The system relies on high-precision three-dimensional modeling and real-time interaction technology to construct a highly realistic virtual clinical operating environment, enabling students to complete the full-process simulation from case analysis to operational training under risk-free conditions. The system integrates three core modules: an intelligent knowledge graph module with three-dimensional visualization capabilities, an interactive clinical case module based on virtual simulation technology, and an intelligent assistant module providing real-time feedback. A group of students was selected to receive instruction using this system and underwent three assessments: theoretical knowledge, clinical reasoning, and practical operations, followed by a satisfaction survey. Results: The students achieved a high excellence rate in assessment scores across the three dimensions of theoretical knowledge, clinical reasoning, and practical operations. The vast majority of students gave high praise to the immersive learning environment constructed by the virtual simulation platform, the authenticity and safety of operational training, and the real-time feedback provided by the intelligent assistant module. Conclusion: The AI case-based virtual simulation intelligent teaching platform constructed in this study provides students with a highly immersive, safe, and repeatable clinical training environment, compensating for prominent issues in traditional teaching such as limited practical resources and high operational risks. This platform achieves the three-dimensional integration of knowledge acquisition, clinical reasoning, and skill training, offering a replicable practical paradigm for the deep integration of artificial intelligence and virtual simulation technology in medical education.
  • PublicationOpen Access
    REVISAGE Analytics: Guiding Writing Through Reflection, Visualization, and Classroom Integration
    (2026) Purwanto, Erick; Xia, Detong; Selig, Thomas; Wu, Jianmao; Atmadja, Natasha
    Background/Context: REVISAGE Analytics was developed at Xi’an Jiaotong-Liverpool University to strengthen academic writing in English-medium courses by pairing AI with explicit pedagogy. The platform embeds reflective learning and teacher oversight into classroom workflows, complementing— rather than replacing—human feedback. Problem/Opportunity: Three issues motivated the innovation: (i) cognitive offloading from “auto-fix” tools that short-circuit reflection; (ii) superficial engagement with generic grammar feedback that neglects structure and diverse learner needs; and (iii) teacher overload and limited visibility into class-wide writing trends. These gaps called for AI that cultivates feedback literacy and inclusion at scale. Methods: A pilot with 30 learners and 6 teachers used classroom deployment and post-use surveys to evaluate usability, engagement, and pedagogical value. Iterative design emphasized visual essay diagrams; rubric-aligned, strengths-based, and customizable feedback; linguistically informed analysis using academic corpora, such as Academic Word List (AWL) and Corpus of Contemporary American English (COCA); accessibility features (dyslexia-friendly mode, text-to-speech); and an AI-plus-teacher group chat. Engagement was measured via self-reported use frequency and session duration; perceived pedagogical value was elicited through teacher comments. Findings/Outcomes: Students reported using the tool two to three times per week with sessions typically exceeding 15 minutes, and all agreed it supported writing reflection and independent revision. Learners highlighted structure visualizations and the dyslexia-friendly mode as especially helpful. Teachers valued adjustable feedback directness, classroom dashboards, and the transparency of the shared AI group chat, noting clearer visibility of rhetorical, lexical, and grammatical issues. Positive sentiment and planned adoption into three writing course sections suggest near-term reach of 200 students; participants recommended a simpler interface and clearer scoring criteria to aid integration. Implications for Practice: REVISAGE functions as a feedback amplifier. It nudges students to diagnose and self-repair, surfaces class-level patterns for targeted instruction, and reduces the burden of individualized comments without sacrificing rigor. Built-in accessibility and visual scaffolds broaden participation for neurodivergent learners. Rubric alignment and analytics support formative assessment, curriculum coherence, and teacher-directed human-AI collaboration.
  • PublicationOpen Access
    Harnessing AI to Transform Adult Learning: A Case Study of iSmartGuide at SUSS
    (2026) Lee, Wee Leong; Huang, Jennifer Mui Kheng; Tan, Ken Kian Xian; Er, Eveleen Ching Wei; Lim, Bryan Yong Tah
    Context: This case study examines the development and implementation of iSmartGuide, an AI- enhanced learning platform launched by the Singapore University of Social Sciences (SUSS) to advance adult education and lifelong learning. As an institution committed to applied learning, SUSS serves a diverse population of working professionals and adult learners. iSmartGuide was developed in response to the limitations of static digital learning resources and the need for a more dynamic, responsive, and inclusive learning environment. Aims: The innovation addresses three core challenges: enabling self-directed and flexible learning; providing personalised and adaptive support; and empowering educators through data-informed teaching. The platform integrates generative AI with retrieval-augmented generation (RAG) to support learners via tools such as an AI Tutor, AI Quiz, accessibility-focused Reader, multilingual support, gamification features, nudging, and analytics dashboards. These features are accessible through SUSS’s learning management system, providing a seamless user experience. Methods: iSmartGuide was developed using a design-based research methodology with iterative phases, including needs analysis, prototyping, stakeholder consultation, pilot testing, and institution-wide rollout. Evaluation methods included platform usage analytics, learner and faculty feedback, and targeted pilot studies across disciplines. Findings: Findings show strong learner engagement during high-stakes periods and broad satisfaction with the platform’s ease of use, relevance, and responsiveness. Students reported that the AI Tutor provided valuable real-time support, while educators used analytics to identify learning gaps and personalise instruction [Klašnja-Milićević et al., 2017]. Accessibility and multilingual tools improved inclusivity, and gamified features supported motivation and continuous engagement. The platform has shifted teaching practice from content transmission to facilitation and intervention, allowing educators to act more responsively and strategically. The AI Tutor alleviates repetitive query handling, freeing time for deeper pedagogical engagement. Implications: Looking ahead, SUSS plans to expand iSmartGuide’s capabilities with features such as smart content recommendations, AI integration with lecture recordings, and voice-activated and emotionally intelligent interactions. The university is also exploring collaborations with other institutions and industry partners to scale the platform for corporate learning and lifelong upskilling, in alignment with national workforce initiatives. This innovation demonstrates how AI can be responsibly leveraged to drive personalised, inclusive, and scalable learning in higher education.

Communities in Camtree Digital Library

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  • Supported by the Sixth Form Colleges Association and the Huish Centre for Practitioner Development, this is a space for research focusing on the unique 16-19 age range, conducted in sixth form colleges by sixth form staff.
  • Cambridge University Press and Assessment's International Education group
  • Camden Learning is a partnership between Camden Schools and Camden Council. It brings education practitioners together, to share expertise, drive improvement and achieve excellent practice.
  • Camtree is the Cambridge Teacher Research Exchange. This community contains peer-reviewed reports of close-to-practice research submitted to Camtree by teacher-researchers who are not associated with another Camtree partner or domain.
  • Research from the College of Education for the Future, part of Beijing Normal University at Zhuhai